Kernel density estimation for circular data: a Fourier series-based plug-in approach for bandwidth selection

Carlos Tenreiro

Journal of Nonparametric Statistics, 2022, vol. 34, issue 2, 377-406

Abstract: In this paper, we derive asymptotic expressions for the mean integrated squared error of a class of delta sequence density estimators for circular data. This class includes the class of kernel density estimators usually considered in the literature, as well as a new class that is closer in spirit to the class of Parzen–Rosenblatt estimators for linear data. For these two classes of kernel density estimators, a Fourier series-based direct plug-in approach for bandwidth selection is presented. The proposed bandwidth selector has a $ n^{-1/2} $ n−1/2 relative convergence rate whenever the underlying density is smooth enough and the simulation results testify that it presents a very good finite sample performance against other bandwidth selectors in the literature.

Date: 2022
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DOI: 10.1080/10485252.2022.2057974

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